Labour Demand Elasticities in Manufacturing Sector in Kenya

Transcription

1 Internatonal Journal of Busness and Socal Scence Volume 8 Number 8 August 2017 Labour Demand Elastctes n Manufacturng Sector n Kenya Anthony Wambugu Unversty of Narob School of Economcs P.O.Box Narob, Kenya Abstract Job creaton and reducton of poverty are often goals of polcy reforms that nvolve removal of nput prce dstortons and acceleraton ofoutput growth. Whether such reforms create jobs depends on the degree of responsveness of labour demand to changes n wages and output. Ths study estmates labour demand functons usng panel data for Kenya manufacturng frms. The estmated elastctes revealed, as expected, that hgher wages reduce employment and hgher value-added ncreases employment. The estmate of own-wage elastcty s around and the output elastcty s around 0.10.The estmates of elastctes are robust to the ncluson of frm-characterstcs and frm fxed effects n the labour demand equaton. The estmates mply that a 10% ncrease n wages reduces employment by 2%, whle an ncrease of a smlar magntude n value added ncreases employment by 1%. Kenyan polcy makers can use these results when consderng polces that ncrease manufacturng ndustry labor costs and value-added. Keywords: Labour demand, wage elastcty, output elastcty, manufacturng, Kenya 1. Introducton Job creaton and poverty reducton reman at the heart of debate n Kenya among polcy makers and the general publc. Ths s because of the potental adverse socal consequences of wdespread unemployment and poverty. A possble explanaton for the falure of the Kenyan economy to create adequate jobs s the level of real wages of formal sector workers. Effcency wage consderatons n a frm s wage polcy and nsttutonal forces (unons, mnmum wage laws) n wage determnaton could result n formal sector real wage at a level above marketclearng wage. Another possble explanaton for poor job creaton s low rates of output growth. After several years of low output growth, the Kenya government launched the Economc Recovery Strategy for Wealth and Employment Creaton n 2003 to jumpstart the economy (GoK, 2003).It was hoped that economc recovery would be accompaned by job creaton. Implct n ths vew, s a postve relatonshp between output and employment. But the magntude of the response s also mportant. In addton, f frms are faced wth hgh wage costs, t may lead to sgnfcant reducton n employment-a negatve relatonshp between wages and employment. In 2007 the Government publshed the Vson 2030, a development blue-prnt that dentfes the manufacturng ndustry as a key ndustry to acheve the Vson s economc growth and job creaton goals (GoK, 2007). Whether ths s realzed or not depends on the responsveness of employment to changes n economc varables. However, there s relatvely lttle emprcal evdence avalable about how manufacturng employers n Kenya adjust employment n response to changes n labor costs and output. Ths paper seeks to contrbute to the current polcy debate n Kenya over employment creaton by nvestgatng the role played by wage costs and value-added n the employment decsons of Kenyan manufacturers. By employment, we mean the number of workers n a frm. The man queston s: how elastc s employment to changes n wage costs and output at frm-level n Kenya s manufacturng ndustry? Kenya provdes sutable context to study the effect of wage costs and output on labor demand. After a long perod of mport substtuton polces, the government began to undertake measures to lberalze the economy n Consequently, many manufacturers began to face competton from mported substtutes. However, labor market regulatons on wages and employment remaned n place untl md 1994 when some measures to lberalze the labor market began to be mplemented. 100

2 ISSN (Prnt), (Onlne) Center for Promotng Ideas, USA There s emprcal evdence (e.g. Bgsten et. al, 2000 and Wambugu, 2003) that manufacturng real wages rose durng the 1990s. But lttle evdence s avalable on how employment responded n Kenya. Frm-level studes of labour demand are also scarce for developng countres (see Roberts and Skoufas, 1997; Teal, 2000). Understandng the responsveness of manufacturng employment to changes n wages and output s mportant for two reasons. Frst, the manufacturng sector n many Afrcan countres ncludng Kenya s a major contrbutor to modern sector employment. Second, publc polces such as those related to socal securty contrbutons, mnmum wages, and redundancy payments change the cost of labour and thus manufacturng employment. If the own-wage elastcty of labour demand s large, polces that ncrease wages costs are lkely to hurt employment substantally. But f labour demand s nelastc; polces that rase wage costs are unlkely to hurt employment substantally. Smlarly, polces that expand output may ether result n substantal job creaton or jobless growth dependng on the elastcty of employment wth respect to output growth. Ths paper used the only avalable frm-level panel data for the manufacturng ndustry n Kenya to estmate frmlevel employment equatons to dentfy the determnants of employment and examne labour demand elastctes. We also analyze the senstvty of parameter estmates on wages and output to estmaton procedures. The data used have several advantages. Frst, the data permt us to overcome the aggregaton problem faced by studes that use aggregate macro-level or ndustry-level data. Second, the data contan a rch set of control varables. Thrd, the panel nature of the data allows control for mpact of unobserved frm-specfc factors on labor demand. The artcles organzed as follows. Secton 2descrbes the methodology of the study, encompassng the analytcal framework, defnton and measurement of varables, descrpton of data and estmaton ssues. The estmaton results are presented n Secton 3. Secton 4 summarzes and concludes the paper. 2. Methodology 2.1 Analytcal Framework The startng pont s the statc proft maxmzaton model of employer behavour (Hammermesh, 1993). Followng Mlner and Wrght (1998), ths study adoptscobb-douglas producton functon. Y A K E (1) WhereY s real output, E represents unts of labour, K s physcal captal stock, and α and βdenote the factor share coeffcents. The parameter γ permts factors that change productve effcency, A. A proft maxmzng employer hres labour and captal untl the margnal revenue product of labour s equal to the wage (w) and margnal productvty of captal s equal to the user cost of captal (r). Solvng the system of margnal productvty relatons to elmnate captal from the output equaton yelds the followng: Y E w A. E (2) r Takng logarthms of (2) and rearrangng allows the dervaton of the frm s demand for labour as: w ln E 0 1 ln 2 lny (3) r where ( ln A ln ln ) 1 0 ; 1 ; 2 ( ) ( ) The responsveness of manufacturng employment to changes n wages and output was examned usng a panel data model. The emprcal counterpart of equaton (3) can be expressed as ln Et t 1 ln wt 2 ln Yt X t vt (4) where 101

3 Internatonal Journal of Busness and Socal Scence Volume 8 Number 8 August E t = total number of workers n frm n tme t w = average real wage n frm n tme t t t Y = real output n frm n tme t X = vector of control varables t = frm specfc effect = tme specfc effect t The error term has three components, that svt t ut. Frst, there are factors that are specfc to the frm, and lkely to affect labour demand, but are not explctly ncluded n the estmatng equaton. The presence of such factors ntroduces unobserved heterogenety among frms and not controllng for the heterogenety njects omtted varable bas. Includng the frm-specfc error component, reduces omtted varable bas from ths source. Although the frm-specfc error component mght be unknown to the researcher, t may be known to frm managers, such that ther employment, wage, and output decsons are a functon of the frm-specfc effect. Ths may lead to smultanety bas. However, f labour supply to a gven frm s perfectly elastc, wages can be treated as exogenous. Gven that the manufacturng sector employs only a small proporton of the labour force n Kenya, the assumpton s plausble. Second, manufacturng labour demand may fluctuate wth changes n the economy over tme. The component represents a tme-specfc effect, to account for busness cycle effects on t employment that are common to all frms. The thrd component be uncorrelated wth explanatory varables. u t represents random factors and s assumed to In addton to the basc varables n the employment equaton, we nclude a number of frm characterstcs n the model as control varables. Frm age s ncluded to account for productvty and effcency n lne wth the model by Jovanovch (1982) that predcts postve correlaton between frm age and productve effcency. Frm-level human captal characterstcs are also ncluded to control for labour qualty, snce frms that pay hgh average wage per employee may be employng hgher qualty labour. Consequently, observable nter-frm dfferences n educaton, worker experence, and tenure of the work force are ncluded n the equaton to reduce the lkelhood that our measure of real wage ndcates qualty of labor. The equaton also controls for sector heterogenety by ncludng dummy varables for sectors, Dummy varables for unon status and foregn ownershp status are ncluded also. Frm locaton dummes are ncluded to accommodate the possblty of systematc varaton n labour demand across urban centers. Although prces for other factors, such as captal are not avalable, some of the varables ncluded (e.g. dummes for sectors and tme perod) may represent ths omtted effect. 2.2 Data and measurement of varables The study used data from surveys of about 200 Kenyan manufacturng frms conducted n under the World Bank s Regonal Program for Enterprse Development (RPED) by the Unversty of Narob and the Unversty of Gothenburg. A follow-up survey was conducted by the Center for the Study of Afrcan Economes and the Unted Natons Industral Development (UNIDO) n New frms were therefore ntroduced to replace those that could not be traced. Survey frms were selected randomly n four sub-sectors of manufacturng actvty: food processng and bakeres, wood and furnture, textle and garment, and metal and machnery. A detaled exposton of survey desgn and a number of studes based on the RPED data for Kenya s provded n Bgsten and Kmuyu (2002) whle Söderbom (2001) provdes a report on the UNIDO survey. The survey team held ntervews wth frm managers and asked questons on frm hstory, entrepreneur characterstcs, fnance, nvestment, output, sales, costs of producton, and employment, and nvestor confdence. Employment s measured as the total number of workers n the frm at the end of year. Human captal varables nclude educaton, age, and tenure. These are weghted averages derved by combnng frm-level nformaton on the occupatonal dstrbuton of workers and ndvdual-level nformaton on educaton, age, and tenure. The weght used s the proporton of workers n a gven occupaton n total work force. Value-added s measured as the value of sales less costs of raw materals and ndrect costs. The sector dummes are defned such that dummy varable= 1 f frm s n the partcular sector and zero otherwse. Locaton dummes are defned such that the dummy varable = 1 f frm located n the partcular cty and zero otherwse.

4 ISSN (Prnt), (Onlne) Center for Promotng Ideas, USA Foregn ownershp status s ndcated by a dummy varable = 1 f frm had any foregn owners and zero otherwse. Unon status s ndcated by a dummy varable = 1 f frm had any unonzed workers and zero otherwse. 3. Emprcal Results 3.1 Descrptve statstcs The summary statstcs (mean, standard devaton, mnmum, maxmum, medan, and the 25% and 75% percentles) for employment, output and wages for the sample are presented n Appendx Table 1.Over half of the survey frms were located n Narob, Kenya s captal cty, 20% are n Mombasa, Kenya s man seaport and the others are n two urban centers (Nakuru and Eldoret) n the nteror. The sze dstrbuton of manufacturers s farly wde: the smallest frm has one worker whle the largest has 4000 workers. The medan manufacturer has 31 workers. Over the survey perod, the medan number of workers rose from 29 to 35 but the mean declned. Overall medan real value added s Kshs. 22, 106. It rose by 16% from Kshs. 20, 374 n wave 1. The mean real value added shows a declne of 37%. The overall medan wage per worker s Kshs. 11, 789. It rose by 62% over the survey perod. 3.2 Estmates of Employment equatons The use of panel data methods allows a rcher analyss than would be possble wth cross-secton data. The paper used standard panel data methods (Baltag, 2008) to estmate the employment equaton. In the pooled OLS, frm fxed effects are gnored, whle n the Fxed Effects model, frm specfc effects are treated as constant across space and tme. The model can be estmated by ncludng a dummy for each frm. Ths s the least square dummy varable (LSDV) model. The dsadvantage s that too many degrees of freedom are lost. Alternatvely, the data can be transformed nto devatons from mean before estmaton. The mportant pont s that n the fxed effects model, the frm effects only affect the ntercepts. In the Random Effects model, frm specfc effects are treated as random draws from an underlyng populaton, and hence requre dstrbutonal assumptons just lke the stochastc error term. The baselne employment equatons are reported n Table 2, column 1 for least squares and column 2 for fxed effects model. The baselne employment equaton ncludes log wage, log value added and allow for tme effects as explanatory varables. Both wage costs and output have coeffcents wth the expected sgns; postve and negatve respectvely. The coeffcents are statstcally sgnfcant at 1% level. Increases n real average wage reduce manufacturers demand for labor, whereas ncreases n output ncrease labour demand. Table 2: Baselne Employment Equatons for Kenyan Manufacturng Sector Ordnary Least Squares (OLS) Fxed Effects (FE estmator) Ln(real wage/employee) (6.12)*** (5.88)*** Ln(real value added) (32.16)*** (5.96)*** Constant (0.04) (12.31)*** Number of Observatons R-squared Number of frms Note. Robust t statstcs n parentheses. * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1% The Hausman specfcaton test rejects the random effects model (χ 2 = ; p-value = 0.00), and therefore the fxed effects model s preferred. Ths means that frm fxed effects only shft the ntercept and are fxed over tme and space. In the pooled OLS the estmated own-wage elastcty s -0.30; smlar to the average own-wage elastcty reported by Hamermesh (1993) for studes of ndustralzed countres. The output elastcty s Wth frm fxed effects (column 2), the own-wage elastcty s and the output elastcty to Ths suggests that gnorng frm fxed effects would overstate labour demand elastctes. 103

5 Internatonal Journal of Busness and Socal Scence Volume 8 Number 8 August 2017 Although the coeffcents on wages and value added n Table 2 have the apror expected sgns, ths may be reflectng the mpact of some frm characterstcs. Therefore the baselne labour demand equaton was augmented wth a set of frm-characterstcs. The results are presented n Table 3.The pooled OLS estmates ndcate that the own-wage elastcty s and the output elastcty s The addton of frm characterstcs reduced the output elastcty. The mpact s more dramatc when we control for frm fxed effects. Whle own wage-elastcty s smlar to that n the baselne equaton (Table 3) the output elastcty falls to The results also show that frms that have relatvely more sklled workforce n terms of educaton and experence, have hgher employment. Further, the effect of wages and output on employment does not seem to reflect a postve correlaton between wages and the aspects of skll we control for. Table 3: Employment Equatons for Kenyan Manufacturng (Wth addtonal frm characterstcs) Ordnary Least Squares Fxed Effects (wthn) Ln(wage/employee) (7.28)*** (5.92)*** Ln(value added) (17.87)*** (5.20)*** Age of frm (0.46) (1.79)* Average educaton (7.08)*** (4.72)*** Average age (4.90)*** (4.37)*** Average tenure (0.46) (1.63) Constant (1.68)* (8.94)*** Number of Observatons R-squared Number of frms Note. Robust t statstcs n parentheses. * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1% The equatons nclude controls for sector, locaton, unon status, foregn ownershp status, and survey wave. The results (not shown) n Table 3 ndcate that the addtve sectoral effects on employment are statstcally nsgnfcant. However, the degree of responsveness of employment to changes n wages and output mght vary across sectors. Ths possblty was explored by addng nteracton terms n the employment equaton to allow employment elastctes to vary across sectors. The results are reported n Appendx Table 2.The results reveal that none of the coeffcents on the nteracton terms are sgnfcant, whch suggests that there are no ntersectoral dfferences n the employment effect of wages or output. The estmates of own-wage elastcty and output elastcty do not dffer markedly from those n Table 3. Because macroeconomc envronment can lead to dfferences n employment responses to wages and output, the employment equaton (4) was extended to nclude nteractons terms to allow employment responses to vary across tme perods. The results are presented n Appendx Table 3.The results show that the own wage elastcty n wave 2 was sgnfcantly dfferent from that n the reference survey wave (wave 1). But the coeffcents on nteracton terms for waves 3 and 4 are not sgnfcantly dfferent from zero. There s some evdence that the output elastcty was lower n wave 2 than n wave 1; a fndng robust to the addton of frm fxed effects. On the other hand, the OLS estmates show that output elastcty n wave 4 was sgnfcantly greater than n wave 1. But ths result s not robust to the ncluson of frm fxed effects. 4. Summary and Concluson The manufacturng ndustry n Kenya, lke n other Afrcan countres, s a source of modern sector employment and output for domestc and export markets. If poverty s gong to be reduced, better payng jobs must be created. 104

6 ISSN (Prnt), (Onlne) Center for Promotng Ideas, USA The manufacturng sector s one of the sectors earmarked to drve Vson 2030, Kenya s development blue-prnt. Hgh wages, labour market regulatons and slow output growth are often blamed for reducng formal employment. In theory, the employment consequences of changes n wages and output depend on the extent to whch employment responds to such changes. However, partly due to lack of sutable data frm-level studes of how wages and output changes would nfluence job creaton efforts n Kenya are lackng. Ths study used frm-level panel data and panel data estmators to obtan wage and output elastctes of employment n Kenya s manufacturng ndustry. Estmates controllng for unobserved frm heterogenety suggest that a 10% ncrease n the average wage reduces employment by about 2%, and a 10% ncrease n value-added ncreases employment by 1%.These results suggest that wage related polces (e.g. ncreases n socal securty and health nsurance contrbutons by employers) that rase wage costs could be expected to have adverse and sgnfcant effect on employment creaton n Kenya manufacturng ndustry. On the other hand polces that ncrease manufacturng value-added could be expected to contrbute to job creaton. References Baltag, H.B.(2008), Econometrc Analyss of panel data, John Wley & Sons Bgsten, A. and Kmuyu, P. (eds), (2002). Structure and performance of manufacturng n Kenya, Palgrave Publshers Lmted, Houndmlls, Basngstoke, Hampshre, New York. Bgsten, A., P.Coller, S.Dercon, B. Gauther, M. Fafchamps, J.W.Gunnng, A.Issacksson,A.Oduro, R.Oostendorp, C. Pattllo, M. Soderbom, F.Teal, A.Zeufack, and S.Appleton (2000). Rates of return to human and physcal captal n Afrca s manufacturng sectors, Economc Development and Cultural Change: 48(4): Government of Kenya (2003). Economc Recovery Strategy for Wealth and Employment Creaton , Narob: Government Prnter. Government of Kenya (2007). Kenya Vson Narob: Government Prnter. Hamermesh, D., (1993). Labor demand, Prnceton, New Jersey: Prnceton Unversty press Jovanovch, B., (1982). Selecton and evoluton of ndustry, Econometrca, 50(3): Republc of Kenya, Economc surveys-varous ssues Mlner, C. and P. Wrght (1998).Modellng labour market adjustment to trade lberalzaton n an ndustralzng economy, The Economc Journal, 108(447): Roberts, M. and Skoufas, E. (1997). The long-run demand for sklled and unsklled labor n Colomban manufacturng plants. The Revew of Economcs and Statstcs, 79(2): Soderbom, M. (2001), Constrants and Opportuntes n Kenyan Manufacturng: Report on the Kenyan Manufacturng Enterprse Survey 2000,Unted Natons Industral DevelopmentOrganzaton Teal, F., (2000). Real wages and the demand for sklled and unsklled male labour n Ghana s manufacturng sector: Journal of Development Economcs, 61(2): Wambugu, A. (2003). Essays on earnngs and human captal n Kenya, Economc studes, no. 124, Department of Economcs, Göteborg Unversty 105

7 Internatonal Journal of Busness and Socal Scence Volume 8 Number 8 August 2017 Appendx Table1: Descrptve statstcs of key varables Employment Real value added Real wage per worker Wave 1 (1993) Mean Standard devaton Mnmum th percentle Medan th percentle Maxmum Number of observatons Wave 2 (1994) Mean Standard devaton Mnmum th percentle Medan th percentle Maxmum Number of observatons Wave 3 (1995) Mean Standard devaton Mnmum th percentle Medan th percentle Maxmum Number of observatons Wave 4 (1999) Mean Standard devaton Mnmum th percentle Medan th percentle Maxmum Number of observatons All ( ) Mean Standard devaton Mnmum th percentle Medan th percentle Maxmum Number of observatons

8 ISSN (Prnt), (Onlne) Center for Promotng Ideas, USA Table 2: Employment Equatons for Kenyan Manufacturng Sector (wth sector nteracton terms) Ordnary Least Squares (OLS) Fxed Effects (wthn) Ln(wage/employee) (3.64)*** (3.05)*** Wage*wood and furnture (0.15) (0.65) Wage*textle and garments (0.45) (0.00) Wage*metal and machnery (0.42) (0.91) Ln(value added) (12.27)*** (3.15)*** Value added*wood and furnture (0.63) (0.84) Value-added*textle and garments (1.47) (0.57) Value-added*metal and machnery (1.16) (1.13) Age of frm (0.15) (1.64) Average educaton (7.19)*** (4.59)*** Average age (5.26)*** (4.20)*** Average tenure (0.59) (1.56) Constant (1.47) (8.46)*** Observatons R-squared Number of frms Note. Robust t statstcs n parentheses. * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1%. The equatons nclude controls for sector, locaton, unon status, foregn ownershp status, and survey wave. 107

9 Internatonal Journal of Busness and Socal Scence Volume 8 Number 8 August 2017 Table 3: Employment Equaton for Kenya Manufacturng Sector (Wth sector & tme nteractons) Ordnary Least Squares (OLS) Fxed Effects (wthn) Ln(wage/worker) (5.03)*** (5.43)*** Wage*wave (2.08)** (2.52)** Wage* wave (0.28) (1.28) Wage* wave (0.75) (0.05) Ln(value added) (12.64)*** (5.93)*** Value-added*wave (2.98)*** (3.71)*** Value-added*wave (0.10) (0.65) Value-added*wave (3.29)*** (0.63) Age of frm (0.69) (0.47) Average educaton (7.67)*** (4.79)*** Average age (5.88)*** (4.11)*** Average tenure (0.93) (1.60) Constant (0.33) (1.89)* Observatons R-squared Number of frms 319 Robust t statstcs n parentheses. * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1%. The equatons nclude controls for sector, locaton, unon status, foregn ownershp status, and survey wave. 108